The main contribution of this paper is a novel deep neural network to tackle online LiDAR-MOS in 3D data. Our method uses a dual-branch structure bridged by motion-guided attention modules to exploit spatial-temporal information from sequential LiDAR scans.
We use a coarse-to-fine architecture fusing range-image and point-cloud representations to reduce the artifacts on the borders of the objects without applying a kNN post-processing and a semantic refinement. Based on that, our method achieves online performance, i.e., performs faster than the frame rate (10 Hz) of a typical 3D LiDAR sensor.
Our method achieves the state-of-the-art LiDAR-MOS performance on the SemanticKITTI-MOS benchmark [1]}.
When using the proposed extra data from KITTI-road sequences, our method gains around 10% improvement on the hidden test of the benchmark. We will release the extra annotated data of the KITTI-road dataset together with the implementation of our method to support future research.
